I want to generate a series of random samples, to do simulations based
on them. Essentially, I want to be able to produce a SAMPLESIZE * N
matrix, where each row of N values consists of either
1. Integers between 1 and M (simulating M rolls of an N-sided die), or
2. A sample of N numbers between 1 and M without repeats (simulating
deals of N cards from an M-card deck).
Example (1) is easy, numpy.random.random_integers(1, M, (SAMPLESIZE, N))
But I can't find an obvious equivalent for (2). Am I missing something
glaringly obvious? I'm using numpy - is there maybe something in scipy I
should be looking at?
Also, in evaluating samples, I'm likely to want to calculate
combinatorial functions, such as the list of all pairs of items from a
sample (imagine looking at how many pairs add up to 15 in a cribbage
hand). Clearly, I can write a normal Python function which does this for
one row, and use apply_along_axis - but that's *slow*. I'm looking for a
function that, given an N*M array and a sample size S, gives a
C(N,S)*S*M array of all the combinations, which runs at array-processing
speeds (preferably without having to code it in C myself!!) Is there
anywhere with this type of function available?
This type of combinatorial simulation seems to me to be a fairly good
fit for numpy's capabilities, and yet I can't seem to find things that
seem relevant. Is it simly not something that people use numpy for? Or
am I looking in the wrong places in the documentation?
Thanks for any help,
Paul.